{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O1t22v2ReiTx"
   },
   "source": [
    "# Application: Gender Bias in Coreference Systems\n",
    "\n",
    "This notebook walks through the analysis in Section 4 of [the paper](https://openreview.net/pdf?id=K0E_F0gFDgA). We'll look at accuracy and bias correlation metrics on the Winogender dataset of [Rudinger et al. 2018](https://arxiv.org/abs/1804.09301), and show how the multibootstrap can be used in two different ways:\n",
    "\n",
    "* A **paired** analysis of an intervention (incremental CDA) applied to pretrained checkpoints.\n",
    "* An **unpaired** analysis comparing to a new set of checkpoints trained with a different procedure (CDA full).\n",
    "\n",
    "This notebook will download pre-computed predictions, which are exactly the predictions used in the paper; the cells below should allow you to directly reproduce Figure 3, Table 1, and Table 2 from Section 4, as well as Figure 5, Figure 6, and Table 4 from Appendix D."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Hz6d1y5qjshN"
   },
   "outputs": [],
   "source": [
    "#@title Import libraries and multibootstrap code\n",
    "import re\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.metrics\n",
    "import scipy.stats\n",
    "\n",
    "from tqdm.notebook import tqdm  # for progress indicator\n",
    "\n",
    "import multibootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "OCD8x6GMh9IQ"
   },
   "outputs": [],
   "source": [
    "#@title Import and configure plotting libraries\n",
    "import matplotlib\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%config InlineBackend.figure_format = 'retina' # make matplotlib plots look better\n",
    "\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yqz5DJZp8P9M"
   },
   "source": [
    "## Download prediction files\n",
    "\n",
    "We release four groups of predictions:\n",
    "\n",
    "* **`base`**: the base MultiBERTs models (`bert-base-uncased`), with 5 coreference runs for each of 25 pretraining checkpoints.\n",
    "* **`cda_intervention-50k`**: as above, but with 50k steps of CDA applied to each checkpoint. 5 coreference runs for each of 25 pretraining checkpoints, paired with `base`.\n",
    "* **`from_scratch`**: trained from-scratch using CDA data. 5 coreference runs for each of 25 pretraining checkpoints, which are not paired with the above.\n",
    "* **`base_extra_seeds`**: 25 coreference runs for each of the first five pretraining seeds from `base`; used in Figure 6.\n",
    "\n",
    "For each group, there are three files:\n",
    "* `run_info.tsv`: run information, with columns `pretrain_seed` and `finetune_seed`\n",
    "* `label_info.tsv` : labels and other metadata for each instance. 720 rows, \n",
    "     one for each Winogender example.\n",
    "* `preds.tsv`: predictions on each instance, with rows aligned to those of\n",
    "    `run_info.tsv` and 720 columns which align to the rows of `label_info.tsv`.\n",
    "\n",
    "The values in `preds.tsv` represent the index of the predicted referent, so for\n",
    "Winogender this means:\n",
    "- 0 is the occupation term\n",
    "- 1 is the other_participant\n",
    "\n",
    "You can also browse these files manually here: https://console.cloud.google.com/storage/browser/multiberts/public/example-predictions/coref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/multiberts_coref/occupations-stats.tsv\r\n",
      "\r\n",
      "/tmp/multiberts_coref/base:\r\n",
      "label_info.tsv\tpreds.tsv  run_info.tsv\r\n",
      "\r\n",
      "/tmp/multiberts_coref/base_extra_seeds:\r\n",
      "label_info.tsv\tpreds.tsv  run_info.tsv\r\n",
      "\r\n",
      "/tmp/multiberts_coref/cda_intervention-50k:\r\n",
      "label_info.tsv\tpreds.tsv  run_info.tsv\r\n",
      "\r\n",
      "/tmp/multiberts_coref/from_scratch:\r\n",
      "label_info.tsv\tpreds.tsv  run_info.tsv\r\n"
     ]
    }
   ],
   "source": [
    "#@title Download predictions and metadata\n",
    "scratch_dir = \"/tmp/multiberts_coref\"\n",
    "if not os.path.isdir(scratch_dir): \n",
    "    os.mkdir(scratch_dir)\n",
    "    \n",
    "preds_root = \"https://storage.googleapis.com/multiberts/public/example-predictions/coref\"\n",
    "GROUP_NAMES = [\n",
    "    'base',\n",
    "    'base_extra_seeds',\n",
    "    'cda_intervention-50k',\n",
    "    'from_scratch'\n",
    "]\n",
    "for name in GROUP_NAMES:\n",
    "    !mkdir -p $scratch_dir/$name\n",
    "    for fname in ['label_info.tsv', 'preds.tsv', 'run_info.tsv']:\n",
    "        !curl -s -O $preds_root/$name/$fname --output-dir $scratch_dir/$name\n",
    "\n",
    "# Fetch Winogender occupations data from official repo https://github.com/rudinger/winogender-schemas\n",
    "!curl -s -O https://raw.githubusercontent.com/rudinger/winogender-schemas/master/data/occupations-stats.tsv \\\n",
    "    --output-dir $scratch_dir\n",
    "        \n",
    "!ls $scratch_dir/**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "POesaG3MEusS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pretrain_seed</th>\n",
       "      <th>finetune_seed</th>\n",
       "      <th>group_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>from_scratch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>from_scratch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>from_scratch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>from_scratch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>from_scratch</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pretrain_seed  finetune_seed    group_name\n",
       "0                0              0          base\n",
       "1                0              1          base\n",
       "2                0              2          base\n",
       "3                0              3          base\n",
       "4                0              4          base\n",
       "..             ...            ...           ...\n",
       "495              9              0  from_scratch\n",
       "496              9              1  from_scratch\n",
       "497              9              2  from_scratch\n",
       "498              9              3  from_scratch\n",
       "499              9              4  from_scratch\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Load run information\n",
    "data_root = scratch_dir\n",
    "\n",
    "all_run_info = []\n",
    "for group_name in GROUP_NAMES:\n",
    "    run_info_path = os.path.join(data_root, group_name, \"run_info.tsv\")\n",
    "    run_info = pd.read_csv(run_info_path, sep='\\t', index_col=0)\n",
    "    run_info['group_name'] = group_name\n",
    "    all_run_info.append(run_info)\n",
    "\n",
    "run_info = pd.concat(all_run_info, axis=0, ignore_index=True)\n",
    "run_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "eBTzk_6eJJN6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group_name\n",
       "base                    125\n",
       "base_extra_seeds        125\n",
       "cda_intervention-50k    125\n",
       "from_scratch            125\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of runs in each group\n",
    "run_info.groupby(by='group_name').apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "iE0AUTCdGJ-9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 720)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Load predictions\n",
    "all_preds = []\n",
    "for group_name in GROUP_NAMES:\n",
    "    preds_path = os.path.join(data_root, group_name, \"preds.tsv\")\n",
    "    all_preds.append(np.loadtxt(preds_path))\n",
    "\n",
    "preds = np.concatenate(all_preds, axis=0)\n",
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "19XrPqgOFjrB"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>pronoun_type</th>\n",
       "      <th>answer</th>\n",
       "      <th>occupation</th>\n",
       "      <th>other_participant</th>\n",
       "      <th>someone</th>\n",
       "      <th>template_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>technician</td>\n",
       "      <td>customer</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MASCULINE</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>technician</td>\n",
       "      <td>customer</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FEMININE</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>technician</td>\n",
       "      <td>customer</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>technician</td>\n",
       "      <td>customer</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MASCULINE</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>technician</td>\n",
       "      <td>customer</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>MASCULINE</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>secretary</td>\n",
       "      <td>visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>FEMININE</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>secretary</td>\n",
       "      <td>visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>secretary</td>\n",
       "      <td>visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>MASCULINE</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>secretary</td>\n",
       "      <td>visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>FEMININE</td>\n",
       "      <td>NOM</td>\n",
       "      <td>1</td>\n",
       "      <td>secretary</td>\n",
       "      <td>visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gender pronoun_type  answer  occupation other_participant  someone  \\\n",
       "0      UNKNOWN          NOM       1  technician          customer    False   \n",
       "1    MASCULINE          NOM       1  technician          customer    False   \n",
       "2     FEMININE          NOM       1  technician          customer    False   \n",
       "3      UNKNOWN          NOM       1  technician          customer     True   \n",
       "4    MASCULINE          NOM       1  technician          customer     True   \n",
       "..         ...          ...     ...         ...               ...      ...   \n",
       "715  MASCULINE          NOM       1   secretary           visitor    False   \n",
       "716   FEMININE          NOM       1   secretary           visitor    False   \n",
       "717    UNKNOWN          NOM       1   secretary           visitor     True   \n",
       "718  MASCULINE          NOM       1   secretary           visitor     True   \n",
       "719   FEMININE          NOM       1   secretary           visitor     True   \n",
       "\n",
       "     template_idx  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "..            ...  \n",
       "715           119  \n",
       "716           119  \n",
       "717           119  \n",
       "718           119  \n",
       "719           119  \n",
       "\n",
       "[720 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Load label info\n",
    "label_info_path = os.path.join(data_root, GROUP_NAMES[0], \"label_info.tsv\")\n",
    "label_info = pd.read_csv(label_info_path, sep='\\t', index_col=0)\n",
    "\n",
    "label_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, load the occupations data from the U.S. Bureau of Labor Statistics, which we'll use to compute the bias correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "dlZpE-dTF4iZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       "accountant       0.5970\n",
       "administrator    0.5486\n",
       "advisor          0.3790\n",
       "appraiser        0.5224\n",
       "architect        0.2081\n",
       "auditor          0.5970\n",
       "baker            0.6080\n",
       "bartender        0.5980\n",
       "broker           0.5550\n",
       "carpenter        0.0207\n",
       "cashier          0.7250\n",
       "chef             0.1960\n",
       "chemist          0.3610\n",
       "clerk            0.6953\n",
       "counselor        0.6648\n",
       "dietitian        0.9460\n",
       "dispatcher       0.5630\n",
       "doctor           0.3790\n",
       "educator         0.7080\n",
       "electrician      0.0230\n",
       "engineer         0.1072\n",
       "examiner         0.6246\n",
       "firefighter      0.0350\n",
       "hairdresser      0.9420\n",
       "hygienist        0.9640\n",
       "inspector        0.0640\n",
       "instructor       0.6230\n",
       "investigator     0.4515\n",
       "janitor          0.3430\n",
       "lawyer           0.3450\n",
       "librarian        0.8300\n",
       "machinist        0.0670\n",
       "manager          0.3851\n",
       "mechanic         0.0180\n",
       "nurse            0.8958\n",
       "nutritionist     0.9460\n",
       "officer          0.3042\n",
       "painter          0.0570\n",
       "paralegal        0.8540\n",
       "paramedic        0.3290\n",
       "pathologist      0.9750\n",
       "pharmacist       0.5700\n",
       "physician        0.3790\n",
       "planner          0.7760\n",
       "plumber          0.0070\n",
       "practitioner     0.7479\n",
       "programmer       0.1835\n",
       "psychologist     0.7030\n",
       "receptionist     0.9060\n",
       "salesperson      0.4808\n",
       "scientist        0.4194\n",
       "secretary        0.9460\n",
       "specialist       0.4135\n",
       "supervisor       0.3864\n",
       "surgeon          0.3790\n",
       "teacher          0.7100\n",
       "technician       0.4034\n",
       "therapist        0.7670\n",
       "veterinarian     0.6050\n",
       "worker           0.3792\n",
       "Name: bls_pct_female, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Load occupations data\n",
    "occupation_tsv_path = os.path.join(data_root, \"occupations-stats.tsv\")\n",
    "\n",
    "# Link to BLS data\n",
    "occupation_data = pd.read_csv(occupation_tsv_path, sep=\"\\t\").set_index(\"occupation\")\n",
    "occupation_pf = (occupation_data['bls_pct_female'] / 100.0).sort_index()\n",
    "occupation_pf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BG-gGRf1_6SN"
   },
   "source": [
    "## Define metrics\n",
    "\n",
    "The values in `preds.tsv` represent binary predictions about whether each of our models predicts that the pronoun corresponds to the occupation term (0) or the other participant (1) in each Winogender example.\n",
    "\n",
    "With this, we can compute two metrics:\n",
    "- Accuracy against binary labels (whether the pronoun should refer to the occupation term, the `answer` column in `label_info`). For this, we'll run bootstrap over all 720 examples.\n",
    "- Correlation of bias score against each occupation's `P(female)`, according to the U.S. Bureau of Labor Statistics. This is done as in [Webster et al. 2020](https://arxiv.org/abs/2010.06032) and [Rudinger et al. 2018](https://arxiv.org/pdf/1804.09301.pdf): for each profession, we compute the fraction of time when female pronouns resolve to it, the fraction of time that male pronouns resolve to it, and take the bias score to be the difference of these two quantities. For this, we'll aggregate to the 60 occupations, then run bootstrap over the set of occupations.\n",
    "\n",
    "These will be used inside the bootstrap, so `get_accuracy()`, `get_bias_corr()`, and `get_bias_slope()` should all take two arguments, aligned lists of labels and predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "_ff54rttLH4R"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6347222222222222\n",
      "Bias r, slope: (0.37916411080542944, 0.3710850348692375)\n"
     ]
    }
   ],
   "source": [
    "#@title Define metrics, test on one run\n",
    "def get_accuracy(answers, binary_preds):\n",
    "    return np.mean(answers == binary_preds)\n",
    "\n",
    "def get_bias_score(preds_row):\n",
    "    df = label_info.copy()\n",
    "    df['pred_occupation'] = (preds_row == 0)\n",
    "    m_pct = df[df[\"gender\"] == \"MASCULINE\"].groupby(by=\"occupation\")['pred_occupation'].agg('mean')\n",
    "    f_pct = df[df[\"gender\"] == \"FEMININE\"].groupby(by=\"occupation\")['pred_occupation'].agg('mean')\n",
    "    return (f_pct - m_pct).sort_index()\n",
    "\n",
    "# Ensure this aligns with result of get_bias_score\n",
    "sorted_occupations = sorted(list(label_info.occupation.unique()))\n",
    "pf_bls = np.array([occupation_pf[occ] for occ in sorted_occupations])\n",
    "\n",
    "def get_bias_corr_and_slope(pf_bls, bias_scores):\n",
    "    lr = scipy.stats.linregress(pf_bls, bias_scores)\n",
    "    return (lr.rvalue, lr.slope)\n",
    "\n",
    "def get_bias_corr(pf_bls, bias_scores):\n",
    "    return get_bias_corr_and_slope(pf_bls, bias_scores)[0]\n",
    "\n",
    "def get_bias_slope(pf_bls, bias_scores):\n",
    "    return get_bias_corr_and_slope(pf_bls, bias_scores)[1]\n",
    "\n",
    "print(\"Accuracy:\" , get_accuracy(label_info['answer'], preds[0]))\n",
    "print(\"Bias r, slope:\", get_bias_corr_and_slope(pf_bls, get_bias_score(preds[0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZdDpm1uhOK-H"
   },
   "source": [
    "Computing the bias scores can be slow because of the grouping operations, so we preprocess all runs before running the bootstrap. This gives us a `[num_runs, 60]` matrix, and we can compute the final bias correlation inside the multibootstrap routine.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Bas4AGViOIF9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 60)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bias_scores = np.stack([get_bias_score(p) for p in preds], axis=0)\n",
    "bias_scores.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R5l1txekUfLA"
   },
   "source": [
    "Finally, attach these to the run info dataframe - this will make it easier to filter by row later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "q9zHBDTsUeje"
   },
   "outputs": [],
   "source": [
    "run_info['coref_preds'] = list(preds)\n",
    "run_info['bias_scores'] = list(bias_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RJmm1EBeQI2Q"
   },
   "source": [
    "## Plot overall scores for each group\n",
    "\n",
    "Before we introduce the multibootstrap, let's get a high-level idea of what our metrics look like by just computing the mean scores for each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "ijYRi6yEPHNm"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>bias_r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>base</th>\n",
       "      <td>0.627044</td>\n",
       "      <td>0.424550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_extra_seeds</th>\n",
       "      <td>0.632244</td>\n",
       "      <td>0.394809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cda_intervention-50k</th>\n",
       "      <td>0.623111</td>\n",
       "      <td>0.263665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>from_scratch</th>\n",
       "      <td>0.622167</td>\n",
       "      <td>0.194511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      accuracy    bias_r\n",
       "group_name                              \n",
       "base                  0.627044  0.424550\n",
       "base_extra_seeds      0.632244  0.394809\n",
       "cda_intervention-50k  0.623111  0.263665\n",
       "from_scratch          0.622167  0.194511"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_info['accuracy'] = [get_accuracy(label_info['answer'], p) for p in preds]\n",
    "rs, slopes = zip(*[get_bias_corr_and_slope(pf_bls, bs) for bs in bias_scores])\n",
    "run_info['bias_r'] = rs\n",
    "run_info['bias_slope'] = slopes\n",
    "\n",
    "run_info.groupby(by='group_name')[['accuracy', 'bias_r']].agg('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that accuracy is very similar across all groups, while - as we might expect - the bias correlation (`bias_r`) decreases significantly for the CDA runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "SWarsCpOsAPp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.7% +/- 1.2%\n"
     ]
    }
   ],
   "source": [
    "# Accuracy across runs\n",
    "data = run_info[run_info.group_name == 'base']\n",
    "desc = data.groupby(by='pretrain_seed').agg(dict(accuracy='mean')).describe()\n",
    "print(f\"{desc.accuracy['mean']:.1%} +/- {desc.accuracy['std']:.1%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also check how much this varies by pretraining seed. As it turns out, not a lot. Here's a plot showing this for the `base` runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "w4Yj7jcdru5U"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f5e6176c910>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 331,
       "width": 885
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Accuracy variation by pretrain run\n",
    "fig = pyplot.figure(figsize=(15, 5))\n",
    "ax = fig.gca()\n",
    "sns.boxplot(ax=ax, x='pretrain_seed', y='accuracy', data=run_info[run_info.group_name == 'base'])\n",
    "ax.set_title(\"Accuracy variation by pretrain seed, base\")\n",
    "ax.set_ylim(0, 1.0)\n",
    "ax.axhline(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a quick check, we can permute the seeds and see if much changes about our estimate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "-gmmvBmQvkCm"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With replacement:    62.5% +/- 0.7%\n",
      "Without replacement: 62.7% +/- 0.8%\n"
     ]
    }
   ],
   "source": [
    "# Accuracy across runs - randomized seed baseline\n",
    "rng = np.random.RandomState(42)\n",
    "data = run_info[run_info.group_name == 'base'].copy()\n",
    "bs = data.accuracy.to_numpy()\n",
    "data['accuracy_bs'] = rng.choice(bs, size=len(bs))\n",
    "desc = data.groupby(by='pretrain_seed').agg(dict(accuracy_bs='mean')).describe()\n",
    "print(f\"With replacement:    {desc.accuracy_bs['mean']:.1%} +/- {desc.accuracy_bs['std']:.1%}\")\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "data = run_info[run_info.group_name == 'base'].copy()\n",
    "bs = data.accuracy.to_numpy()\n",
    "rng.shuffle(bs)\n",
    "data['accuracy_bs'] = bs\n",
    "desc = data.groupby(by='pretrain_seed').agg(dict(accuracy_bs='mean')).describe()\n",
    "print(f\"Without replacement: {desc.accuracy_bs['mean']:.1%} +/- {desc.accuracy_bs['std']:.1%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dWonjiXUQuaC"
   },
   "source": [
    "## Figure 5 (Appendix): Bias correlation for each pre-training seed\n",
    "\n",
    "Let's do the same as above, but for bias correlation. Again, this is on the whole run - no bootstrap yet - but should give us a sense of the variation you'd expect if you were to run this experiment ad-hoc on different pretraining seeds. As above, we'll just show the `base` runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "sy2rcYNQhC--"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 449,
       "width": 903
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(15, 7))\n",
    "ax = fig.gca()\n",
    "base = sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == 'base'], palette=['darkslategray'])\n",
    "ax.set_title(\"Winogender bias correlation (r) by pretrain seed\")\n",
    "ax.set_ylim(-0.2, 1.0)\n",
    "ax.axhline(0)\n",
    "\n",
    "legend_elements = [matplotlib.patches.Patch(facecolor='darkslategray', label='Base')]\n",
    "ax.legend(handles=legend_elements, loc='upper right', fontsize=14)\n",
    "\n",
    "ax.title.set_fontsize(16)\n",
    "ax.set_xlabel(\"Pretraining Seed\", fontsize=14)\n",
    "ax.tick_params(axis='x', labelsize=14)\n",
    "ax.set_ylabel(\"Bias correlation (r)\", fontsize=14)\n",
    "ax.tick_params(axis='y', labelsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "1gH2_un2tIj1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.018444444444444485, 0.01766666666666672]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected range of accuracy if we randomly sampled data\n",
    "import scipy.stats\n",
    "[n/720.0 - 0.624 for n in scipy.stats.binom.interval(0.682, 720, 0.624)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SrCRb2IdQSV5"
   },
   "source": [
    "## Figure 3: Bias correlation by pretrain seed, base and CDA intervention\n",
    "\n",
    "Now let's compare the `base` runs to running CDA for 50k steps. Again, no bootstrap yet - just plotting scores on full runs, to get a sense of how much difference we might expect to see if we did this ad-hoc and measured the effect size of CDA using just a single pretraining run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "TJjYfmCCwjyC"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 449,
       "width": 903
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expt_group = \"cda_intervention-50k\"\n",
    "\n",
    "fig = pyplot.figure(figsize=(15, 7))\n",
    "ax = fig.gca()\n",
    "base = sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == 'base'], palette=['darkslategray'])\n",
    "expt = sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == expt_group], palette=['lightgray'])\n",
    "ax.set_title(\"Winogender bias correlation (r) by pretrain seed\")\n",
    "ax.set_ylim(-0.2, 1.0)\n",
    "ax.axhline(0)\n",
    "\n",
    "legend_elements = [matplotlib.patches.Patch(facecolor='darkslategray', label='Base'),\n",
    "                   matplotlib.patches.Patch(facecolor='lightgray', label='CDA-incr')]\n",
    "ax.legend(handles=legend_elements, loc='upper right', fontsize=14)\n",
    "\n",
    "ax.title.set_fontsize(16)\n",
    "ax.set_xlabel(\"Pretraining Seed\", fontsize=14)\n",
    "ax.tick_params(axis='x', labelsize=14)\n",
    "ax.set_ylabel(\"Bias correlation (r)\", fontsize=14)\n",
    "ax.tick_params(axis='y', labelsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "88zyvSdBIUb7"
   },
   "source": [
    "## Appendix D: Cross-Seed Variation\n",
    "\n",
    "You might ask: how much of this variation is actually due to the coreference task training? We can see decently large error bars for each pretraining seed above, and we only had five coreference runs each.\n",
    "\n",
    "One simple test is to ignore the pretraining seed. We'll create groups by randomly sampling (with replacement) five runs from the set of runs we have, then looking at the variance in the metrics. We can see that for `bias_r`, the variance is about 4x as high when using the real seeds (stdev = 0.097 vs 0.049), suggesting that most of the variation does in fact come from pretraining variation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "h64-_huFHhr5"
   },
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>finetune_seed</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>bias_r</th>\n",
       "      <th>bias_slope</th>\n",
       "      <th>bias_r_bs_0</th>\n",
       "      <th>bias_r_bs_1</th>\n",
       "      <th>bias_r_bs_2</th>\n",
       "      <th>bias_r_bs_3</th>\n",
       "      <th>bias_r_bs_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.627044</td>\n",
       "      <td>0.424550</td>\n",
       "      <td>0.450015</td>\n",
       "      <td>0.431620</td>\n",
       "      <td>0.432430</td>\n",
       "      <td>0.431666</td>\n",
       "      <td>0.415806</td>\n",
       "      <td>0.425594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012221</td>\n",
       "      <td>0.096832</td>\n",
       "      <td>0.108169</td>\n",
       "      <td>0.049701</td>\n",
       "      <td>0.052906</td>\n",
       "      <td>0.045431</td>\n",
       "      <td>0.045838</td>\n",
       "      <td>0.051212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      finetune_seed  accuracy    bias_r  bias_slope  bias_r_bs_0  bias_r_bs_1  \\\n",
       "mean            2.0  0.627044  0.424550    0.450015     0.431620     0.432430   \n",
       "std             0.0  0.012221  0.096832    0.108169     0.049701     0.052906   \n",
       "\n",
       "      bias_r_bs_2  bias_r_bs_3  bias_r_bs_4  \n",
       "mean     0.431666     0.415806     0.425594  \n",
       "std      0.045431     0.045838     0.051212  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = run_info[run_info.group_name == 'base'].copy()\n",
    "bs = data.bias_r.to_numpy()\n",
    "for i in range (5):\n",
    "    rng = np.random.RandomState(i)\n",
    "    data[f'bias_r_bs_{i}'] = rng.choice(bs, size=len(bs))\n",
    "    \n",
    "data.groupby(by='pretrain_seed').agg('mean').describe().loc[['mean', 'std']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QWR4FJkcd5kw"
   },
   "source": [
    "### Figure 6: Extra task runs\n",
    "\n",
    "Another way to test this is to look at the `base_extra_seeds` runs, where we ran 5 different pretraining seeds with 25 task runs. This gives us a better estimate of the mean for each pretraining seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "9YJpkva0d4rf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Bias correlation (r)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 449,
       "width": 504
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(8, 7))\n",
    "ax = fig.gca()\n",
    "sns.boxplot(ax=ax, x='pretrain_seed', y='bias_r', data=run_info[run_info.group_name == 'base_extra_seeds'])\n",
    "ax.set_title(\"Bias variation by pretrain seed, base w/extra seeds\")\n",
    "ax.set_ylim(-0.2, 1.0)\n",
    "ax.axhline(0)\n",
    "\n",
    "ax.title.set_fontsize(16)\n",
    "ax.set_xlabel(\"Pretraining Seed\", fontsize=14)\n",
    "ax.tick_params(axis='x', labelsize=14)\n",
    "ax.set_ylabel(\"Bias correlation (r)\", fontsize=14)\n",
    "#ax.tick_params(axis='y', labelsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can also use the multibootstrap as a statistical test to check for differences between these seeds. We'll compare seed 0 to seed 1, and do an unpaired analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cellView": "form",
    "id": "la21nz6EeOeD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available runs: 50\n",
      "Computing bias r\n",
      "Labels: float64 (60,)\n",
      "Preds: float64 (50, 60)\n",
      "Multibootstrap (unpaired) on 60 examples\n",
      "  Base seeds (1): [0]\n",
      "  Base: 25 runs\n",
      "  Expt seeds (1): [1]\n",
      "  Expt: 25 runs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9eee2ba38ed44b8ae2cbef4c2254769",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.368 with 95% CI of (0.181 to 0.54)\n",
      "  E[L'] = 0.571 with 95% CI of (0.409 to 0.699)\n",
      "  E[L'-L] = 0.203 with 95% CI of (0.0334 to 0.378); p-value = 0.009\n"
     ]
    }
   ],
   "source": [
    "#@title Bootstrap to test if seed 1 is different from seed 0\n",
    "num_bootstrap_samples = 1000  #@param {type: \"integer\"}\n",
    "rseed=42\n",
    "\n",
    "mask = (run_info.group_name == 'base_extra_seeds')\n",
    "mask &= (run_info.pretrain_seed == 0) | (run_info.pretrain_seed == 1)\n",
    "selected_runs = run_info[mask].copy()\n",
    "\n",
    "# Set intervention and seed columns\n",
    "selected_runs['intervention'] = (selected_runs.pretrain_seed == 1)\n",
    "selected_runs['seed'] = selected_runs.pretrain_seed\n",
    "print(\"Available runs:\", len(selected_runs))\n",
    "\n",
    "##\n",
    "# Compute bias r\n",
    "print(\"Computing bias r\")\n",
    "labels = pf_bls.copy()\n",
    "print(\"Labels:\", labels.dtype, labels.shape)\n",
    "preds = np.stack(selected_runs.bias_scores)\n",
    "print(\"Preds:\", preds.dtype, preds.shape)\n",
    "\n",
    "metric = get_bias_corr\n",
    "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                        metric, nboot=num_bootstrap_samples,\n",
    "                                        paired_seeds=False,\n",
    "                                        rng=rseed,\n",
    "                                        progress_indicator=tqdm)\n",
    "\n",
    "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5sPsiEiShk3E"
   },
   "source": [
    "## Section 4.1 / Table 1: Paired analysis: base vs. CDA intervention\n",
    "\n",
    "We've seen how much variation there can be across pretraining checkpoints, so let's use the multibootstrap to help us get a better estimate of the effectiveness of CDA. Here, we'll look at CDA for 50k steps as an intervention on the base checkpoints, and so we'll perform a paired analysis where we sample the same pretraining seeds from both sides.\n",
    "\n",
    "base (`L`) is MultiBERTs following the original BERT recipe, and expt (`L'`) has additional steps with counterfactual data applied to these same checkpoints. We have 25 pretraining seeds on base and the same 25 pretraining seeds on expt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "u-QwZsXmWx0I"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available runs: 250\n",
      "Computing accuracy\n",
      "Labels: int64 (720,)\n",
      "Preds: float64 (250, 720)\n",
      "Multibootstrap (paired) on 720 examples\n",
      "  Common seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Base: 125 runs\n",
      "  Expt: 125 runs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d505e3c19af547bbb228816743f95634",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.626 with 95% CI of (0.599 to 0.654)\n",
      "  E[L'] = 0.623 with 95% CI of (0.594 to 0.651)\n",
      "  E[L'-L] = -0.00372 with 95% CI of (-0.0129 to 0.00579); p-value = 0.21\n",
      "\n",
      "Computing bias r\n",
      "Labels: float64 (60,)\n",
      "Preds: float64 (250, 60)\n",
      "Multibootstrap (paired) on 60 examples\n",
      "  Common seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Base: 125 runs\n",
      "  Expt: 125 runs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21948ecc827443339f504aa38e1a71b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.423 with 95% CI of (0.29 to 0.548)\n",
      "  E[L'] = 0.261 with 95% CI of (0.115 to 0.395)\n",
      "  E[L'-L] = -0.162 with 95% CI of (-0.261 to -0.0672); p-value = 0.001\n"
     ]
    }
   ],
   "source": [
    "num_bootstrap_samples = 1000  #@param {type: \"integer\"}\n",
    "rseed=42\n",
    "\n",
    "expt_group = \"cda_intervention-50k\"\n",
    "\n",
    "mask = (run_info.group_name == 'base')\n",
    "mask |= (run_info.group_name == expt_group)\n",
    "selected_runs = run_info[mask].copy()\n",
    "\n",
    "# Set intervention and seed columns\n",
    "selected_runs['intervention'] = selected_runs.group_name == expt_group\n",
    "selected_runs['seed'] = selected_runs.pretrain_seed\n",
    "print(\"Available runs:\", len(selected_runs))\n",
    "\n",
    "all_samples = {}\n",
    "\n",
    "##\n",
    "# Compute accuracy\n",
    "print(\"Computing accuracy\")\n",
    "labels = np.array(label_info['answer'])\n",
    "print(\"Labels:\", labels.dtype, labels.shape)\n",
    "preds = np.stack(selected_runs.coref_preds)\n",
    "print(\"Preds:\", preds.dtype, preds.shape)\n",
    "\n",
    "metric = get_accuracy\n",
    "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                        metric, nboot=num_bootstrap_samples,\n",
    "                                        paired_seeds=True,\n",
    "                                        rng=rseed,\n",
    "                                        progress_indicator=tqdm)\n",
    "all_samples['accuracy'] = samples\n",
    "multibootstrap.report_ci(all_samples['accuracy'], c=0.95, expect_negative_effect=True);\n",
    "\n",
    "print()\n",
    "\n",
    "##\n",
    "# Compute bias r\n",
    "print(\"Computing bias r\")\n",
    "labels = pf_bls.copy()\n",
    "print(\"Labels:\", labels.dtype, labels.shape)\n",
    "preds = np.stack(selected_runs.bias_scores)\n",
    "print(\"Preds:\", preds.dtype, preds.shape)\n",
    "\n",
    "metric = get_bias_corr\n",
    "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                        metric, nboot=num_bootstrap_samples,\n",
    "                                        paired_seeds=True,\n",
    "                                        rng=rseed,\n",
    "                                        progress_indicator=tqdm)\n",
    "all_samples['bias_r'] = samples\n",
    "\n",
    "multibootstrap.report_ci(all_samples['bias_r'], c=0.95, expect_negative_effect=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5MErilSi66WN"
   },
   "source": [
    "### Plot result distribution\n",
    "\n",
    "It can also be illustrative to look directly at the distribution of samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "cellView": "form",
    "id": "wKl-sV2vzGau"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.423 with 95% CI of (0.29 to 0.548)\n",
      "  E[L'] = 0.261 with 95% CI of (0.115 to 0.395)\n",
      "  E[L'-L] = -0.162 with 95% CI of (-0.261 to -0.0672); p-value = 0.001\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 439,
       "width": 885
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Bias r\n",
    "columns = ['Base', 'CDA intervention']\n",
    "var_name = 'Group Name'\n",
    "val_name = \"Bias Correlation\"\n",
    "samples = all_samples['bias_r']\n",
    "\n",
    "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n",
    "\n",
    "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "fig = pyplot.figure(figsize=(10, 7))\n",
    "ax = axs[0]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n",
    "ax.set_title(\"MultiBERTs CDA intervention - bias r\")\n",
    "ax.axhline(0)\n",
    "\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"Accuracy delta\"\n",
    "bdf = pd.DataFrame(samples, columns=columns)\n",
    "bdf['deltas'] = bdf['CDA intervention'] - bdf['Base']\n",
    "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "ax = axs[1]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n",
    "            palette='gray')\n",
    "ax.set_title(\"MultiBERTs CDA intervention - bias r deltas\")\n",
    "ax.axhline(0)\n",
    "\n",
    "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xZW4aD1_K2S6"
   },
   "source": [
    "## Section 4.2 / Table 2: Unpaired analysis: CDA intervention vs. CDA from-scratch\n",
    "\n",
    "Here, we'll compare our CDA 50k intervention to a set of models trained from-scratch with CDA data.\n",
    "\n",
    "base (`L`) is the intevention CDA above, and expt (`L'`) is a similar setup but pretraining from scratch with the counterfactually-augmented data. We have 25 pretraining seeds on base and 25 pretraining seeds on expt, but these are independent runs so we'll do an unpaired analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "o81OG303Yi5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available runs: 250\n",
      "Computing accuracy\n",
      "Labels: int64 (720,)\n",
      "Preds: float64 (250, 720)\n",
      "Multibootstrap (unpaired) on 720 examples\n",
      "  Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Base: 125 runs\n",
      "  Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Expt: 125 runs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca56d628e23b4d199bea07d19b0b707f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.623 with 95% CI of (0.592 to 0.65)\n",
      "  E[L'] = 0.622 with 95% CI of (0.592 to 0.649)\n",
      "  E[L'-L] = -0.00112 with 95% CI of (-0.0113 to 0.00908); p-value = 0.416\n",
      "\n",
      "Computing bias r\n",
      "Labels: float64 (60,)\n",
      "Preds: float64 (250, 60)\n",
      "Multibootstrap (unpaired) on 60 examples\n",
      "  Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Base: 125 runs\n",
      "  Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Expt: 125 runs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5813901c89ce4b1f86e2b73b0371b8ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.256 with 95% CI of (0.115 to 0.391)\n",
      "  E[L'] = 0.192 with 95% CI of (0.0678 to 0.318)\n",
      "  E[L'-L] = -0.0639 with 95% CI of (-0.175 to 0.05); p-value = 0.132\n"
     ]
    }
   ],
   "source": [
    "num_bootstrap_samples = 1000  #@param {type: \"integer\"}\n",
    "rseed=42\n",
    "\n",
    "base_group = \"cda_intervention-50k\"\n",
    "expt_group = \"from_scratch\"\n",
    "\n",
    "mask = (run_info.group_name == base_group)\n",
    "mask |= (run_info.group_name == expt_group)\n",
    "selected_runs = run_info[mask].copy()\n",
    "\n",
    "# Set intervention and seed columns\n",
    "selected_runs['intervention'] = selected_runs.group_name == expt_group\n",
    "selected_runs['seed'] = selected_runs.pretrain_seed\n",
    "print(\"Available runs:\", len(selected_runs))\n",
    "\n",
    "all_samples = {}\n",
    "\n",
    "##\n",
    "# Compute accuracy\n",
    "print(\"Computing accuracy\")\n",
    "labels = np.array(label_info['answer'])\n",
    "print(\"Labels:\", labels.dtype, labels.shape)\n",
    "preds = np.stack(selected_runs.coref_preds)\n",
    "print(\"Preds:\", preds.dtype, preds.shape)\n",
    "\n",
    "metric = get_accuracy\n",
    "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                        metric, nboot=num_bootstrap_samples,\n",
    "                                        paired_seeds=False,\n",
    "                                        rng=rseed,\n",
    "                                        progress_indicator=tqdm)\n",
    "all_samples['accuracy'] = samples\n",
    "multibootstrap.report_ci(all_samples['accuracy'], c=0.95, expect_negative_effect=True);\n",
    "\n",
    "print()\n",
    "\n",
    "##\n",
    "# Compute bias r\n",
    "print(\"Computing bias r\")\n",
    "labels = pf_bls.copy()\n",
    "print(\"Labels:\", labels.dtype, labels.shape)\n",
    "preds = np.stack(selected_runs.bias_scores)\n",
    "print(\"Preds:\", preds.dtype, preds.shape)\n",
    "\n",
    "metric = get_bias_corr\n",
    "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                        metric, nboot=num_bootstrap_samples,\n",
    "                                        paired_seeds=False,\n",
    "                                        rng=rseed,\n",
    "                                        progress_indicator=tqdm)\n",
    "all_samples['bias_r'] = samples\n",
    "\n",
    "multibootstrap.report_ci(all_samples['bias_r'], c=0.95, expect_negative_effect=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "cellView": "form",
    "id": "mMKexMXRL7-_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.256 with 95% CI of (0.115 to 0.391)\n",
      "  E[L'] = 0.192 with 95% CI of (0.0678 to 0.318)\n",
      "  E[L'-L] = -0.0639 with 95% CI of (-0.175 to 0.05); p-value = 0.132\n"
     ]
    },
    {
     "data": {
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8kvTRRx+pS5cuSktLU3JyskJDQ7Vq1So99dRT+vTTT0v0mp4Jt9utBx54QGPGjFFERISaNWumLVu2aNCgQbr//vuDNnCDHePRRx/Vc889p/nz5ysqKkqpqamKiopSWlqa3nvvPf3jH/8IuNiYk5OjBx98UO+99542btyoevXqKTk5Wbt27dJnn32m2267rVifZX+jRo3SsGHDVKNGDSUlJSkyMvKUF3sBoLKj3VZ+aLfRbiuIdhvtNv8y7TbabWWpurfbDh48qPvuu0/Dhg1Tenq6UlJS5PP5NGzYMHXp0qXI/SZPnqw777xTM2bMUHZ2thwOhwzD0DfffKN77rlHX3755SnjbdeunTmXaY0aNdSuXTvzvSbl3eRwxx136Msvv9SRI0fUrFkz1a9fX/v379eMGTN07733Fvr8ffTRR+ratat++ukn2e12paamyufz6fvvv9e///3vIr9Xi80AgGpoyZIlhsPhMC6++GLDMAxj0KBBhsPhMPr06VNo20mTJhkOh8MYNWqUkZWVZTgcDsPhcBg7d+40txkyZIjhcDiMbt26BX28fv36GQ6Hw3j++efNup07d5rHysrKKvR4t99+e6Hj5G+/fv36Qo/9wAMPBGzr8/mM48ePG6tXrzb+9a9/GQ6Hw3jiiSeKfH4dOnQo6uU6qeLs7/V6jTlz5hjt2rUzHA6H0aNHj5Me0/+18X+d84916aWXGg6Hw5g5c2bAuilTphipqalGq1atCu0XzLx588zH2bt37ym3L0pJX8PXX3/dcDgcxt///vdC6zIyMoyzzjrLcDgcxs8//2zWz5w503A4HMZZZ51lHDx4sNixBXvPPP/882b9I488Yhw4cMBcN3r0aHPd6tWrT3kswzCM7777znA4HEa7du0C/iYul8t47733zHW7du0y1+W/ZvmfG5fLZT5/wzCMp59+2nA4HMa7775b6Dn99ttvhsPhMC655BJzv6NHjxrt27c3HA6H8fLLLxvHjh0zt1+xYoVx+eWXGw6Hw/j0008DjtWhQwczjoEDBxq5ubmGYRhGbm6uGcMFF1xgPo5hFP7+CHa8H374waybMmWK+bf78ssvDZ/PZxiGYXg8HuOjjz4yUlNTjdTUVGPhwoWFHsPhcBiXXXaZsXjxYnPdpk2bjMsuu8xwOBxGr169CsUQTElfz/zvrO7duxvHjx83t92xY4dx3XXXGQ6Hwxg2bFixHvtkgn03Gsap3x9btmwxzjnnHMPhcBivv/664XQ6zX3nz59vXHjhhUGfb/4x27RpY0yZMsWsX7ZsmZGammo4HA6jVatWxjPPPGM+76ysLOPee+81HA6H0aVLl2I/tw0bNhgOh8P4y1/+YmzYsMGsz83NNXr16mU4HA7jmmuuCdgn/7N5/fXXB3zO0tLSzL/5+++/X+j5XHLJJcbWrVsNwzCM48ePGzk5OYZhGMbtt99uOBwO46mnngr4TBw7dsx45plnSvz+Lml8/p+vfv36mX+n3Nxco0ePHobD4TDatm0b8Phlwf/zdO655xpz5swx123YsMGMs2fPngH7Bfs8f/bZZ4bD4TAuvfRS448//gjYftasWUbr1q0L/T6NHTvWcDgcxn333WdkZmaa9QcPHjTuuecew+FwGC+++GKxnov/97f/91n+ZwMAqhrabYWfH+022m2GQbuNdhvtNtpttNsKxl9V2m1PPfWU4XA4jNtuuy3g+/jrr782f3cK/u6uXr3aaNOmjdGmTRtj7NixhtfrNQwj7xzjiy++MM466yyjTZs2xm+//WbuU9R7qKhzpQMHDhjnnXee4XA4jJEjRxoej8dct2PHDuPWW281HA6H8Z///Mesz8zMNM466yzjnHPOMZYtW2bWezwe44MPPjAcDodx/vnnB3w3lBQ9OAFAJ4Y7mjNnTqF1+cMc5W9TUS1btswcliI1NVWtWrVS27Ztdffdd2vx4sVq37693n333SL337VrV8D+Rf2bPHly0P0PHDig++67r9C/2267TZdccom6dOmirKwsXX755XrttddO+3lmZGTo4MGDSkhI0I033hiw7rbbbtM999yjm266qVhDPhw9etRcTkxMPO2YSiomJkaSgg4XM2PGDLndbtWuXVsXX3yxWd+hQwdFR0fL7XZrypQppRJHjRo1NHjwYNWqVcus69y5s3kX4a+//lqs4wwePFiS9NJLL6lTp05mfVhYmJ544gndeOONysrKCrij1t/TTz+tsLAwSVLNmjUlnbgjNdhQKPl3rd50003mfhMnTtSePXt08cUX6/XXXw+4I+6CCy7QG2+8ISnvzrFgdypeccUVevrpp805RcLDw/Xcc89Jyhsa5UzmHsof3qN79+666667zCG/QkJC9N///lcPPvigDMPQoEGDgu7/0ksvBdypnpKSovvvv19S8f9GJX0984fGuuWWWwKGR2vcuLGeffZZXX311eX2mQn2/hgxYoRyc3N1xRVX6JVXXlFUVJS5/ZVXXqm+fftKkj799FNlZmYWOubtt9+u2267zSznDz0j5Q1p1q9fP/N5x8TE6L777pOUd2dtceW/hm3btlXLli3N+vz31mWXXaa//OUvOn78uCRp3759mjp1qmw2m4YNGxZwl2RqaqpeeeUVSdK0adMKPdY999wT0IskIiJCu3fv1q5duxQZGanXXnst4DMRGxurZ599VlLe+7s4Q+2cSXx/+ctf9Pzzz5t/p/DwcL300kuS8u52L+oO9bLw3HPP6dprrzXLLVu2VP/+/SXl3fF66NChk+6/ZMkShYSEqFu3bmrVqlXAuhtvvNH83vb/zli/fr2kvN4dNWrUMOuTkpL0wgsvqH379mrYsGGJnkfdunXNYamkE58NAKjqaLfRbqPdlod2G+02iXYb7bbCaLflqazttl27dmnWrFkKCQnR4MGD1aBBA3PdLbfcokceeSTofsOGDZPH49Gjjz6qf/zjH7Lb89J+NptNd999t/75z3/K4/Fo+PDhJYrf37JlyyRJ559/vh5++GGFhISY6xo3bqyHH35YUuBrum3bNrndbjVr1kwXXXSRWR8SEqLHHntM1113nW644YYzGk779MZpAIAqplWrVkpOTtb27du1bt06tWnTRlLeCcTixYt1/vnnq169euYJTUUUGxsbcOIiSdnZ2dq/f78yMjI0f/58Pfnkk3r77beDDocQHh5erOGHkpKSgta7XK6gQ0VJeT/gd955p2644QZdeeWVxXg2RUtMTFRcXJyOHDmil156SQ8//HDASWjv3r2LfSz/k2uPx2M2kspafkPNf26bfPknmTfeeKN5QiLlxXr11VdrxowZ+uqrr/Sf//znjOO4+OKLFRkZWai+WbNm2rFjR7EuNuzYsUObNm2S3W4PaCT7u/nmm/XNN99owYIF5slxvho1aqhZs2aF9rnssstUq1Ytbdu2TWvWrDHfmz6fz5yvx3+Yox9++EGS1KlTp6Cv65VXXqmEhARlZGRo7dq1ZqMoX/v27QvtU69ePUVFRSk7O/u057bbvHmzdu7cKbvdrr///e9Bt+ncubM+++wz/fbbb8rIyAj4jNlstqCfmfzXrLhxlfT1zL9YMmDAAIWFhemvf/2r+fm4/vrri5zXqrQV9f5YsGCBJJkXDAq69tpr1aBBA+3evVtLliwpdFEt2GvasGFDrVq1SpdcckmhoczyLyaV5DegcePGkqT58+fr448/1i233GIO7RQVFWXOY5Nv/vz5MgxD559/fsB3Wr5rrrlG06ZNMxvE/gq+nyWpQYMGWrp0qXJycoJ+zv3rgs1jUtCZxOffMM2XlJSkGjVq6PDhw2c8N1BxhYeH64477ihUf+GFF5rnAEuXLi30fvH33nvvye12B/2e8Xq95oXQ7Oxssz7/8/Txxx+rTp06at++vbld27ZtA4abKq7zzjsv4DcCAKoL2m2022i3nUC7LQ/tNtpt+Wi30W7LV1nbbflzUp533nlmPP7uuusuvffeewF1ubm5Wrx4saS8GyCCufnmm/Xxxx9r8eLF8ng8pzV8e6dOndSpU6ci53DN/632f00bNmyokJAQpaWlacCAAbr33nvNz5x04saSM0GCEwD+dP3112vEiBGaPXu22VD+/vvv5Xa7dcMNN1gc3am1adNGY8aMCbru119/VY8ePfTDDz+oS5cuGj16dKFtateurQkTJpz24zds2NBsrEh5J/BTp07VO++8oyNHjig+Pr5U5pEJDQ1V9+7d9eabb2ry5MmaPHmy6tevr8suu0zt27fXFVdcEdAAPhn/O2AzMzMD7ngsS/mNm/j4+ID6zZs3mxN/BzspufnmmzVjxgxt3bpVK1as0IUXXnhGcRQ1N1H+CbTP5zvlMTZt2iRJstvt5t1aBeWfhG/fvl2GYQScYNauXTvoPqGhobrppps0evRozZw502zYLV++XPv27VPTpk117rnnmtvn3yE2ZswYff3110GPmX+BYuvWrYUaFkW9FhEREcrOzi7WaxHMtm3bJEmNGjUq9PfO17hxY8XFxenYsWPatm1bQEM5Ojo66IWt/L9RsLlKginp6/nwww9r1qxZ2rp1qx555BFFRUXpwgsv1BVXXKFrrrlGjRo1Ktbjnqlg74+srCwdOHBAkszv6mBat26t3bt3m38Df8H+3gXvNvaXf/JvFDEXUDDnnnuurrvuOs2ZM0dvv/223n77baWmpuryyy9Xhw4ddMEFFwQ0dHbs2CFJQRuh+fEVvPM0X1GfIynvvbJ582b99ttv2r59u3bu3KmNGzean12peJ/1M4mvTp06QetjYmJ0+PDhIhtIpa1p06ZF/j60aNFC27dv19atW095nLCwMB05ckQrVqzQli1blJ6eri1btmjdunXm97v/e+Xuu+/WxIkTtWPHDvPO9rZt2+qKK67Q1VdfrRYtWpT4uZzsbw4AVR3tNtpttNvy0G47gXYb7TaJdtuZxke7zfp2W/7noKjHatCggfk95L9P/vfmSy+9FDShmv/ecTqd2rdvX4l7ovoLCwvTL7/8og0bNmjHjh3asWOH0tLSlJ6eHvBYUt7zf/DBBzVq1CiNGDFCI0aMUNOmTXX55Zerffv2+utf/2p+pk8XCU4A+FN+Q3nOnDl66qmnJEnfffddpRjm6FTOP/989e/fX//4xz+0ZMmSUmlknUpsbKweeOABNW/eXP/5z3/06aefyuPxmMNhnInOnTsrOTlZo0aN0rJly7Rnzx599dVX+uqrrxQTE6P//Oc/euKJJ055nOTkZNlsNhmGoU2bNhXrB37Xrl2Kjo4+o2Fe8k/ECt7hOHXqVHP53nvvPekxvvzyyzP+G57qJKI4jYL8k0KPx1PkneD5fD6fjh8/HtDwO9nd13/72980evRoffvtt3ruuedks9nMYXluvfXWoHEUZ0iiYBPSl8ZrEUz+naP5d/0VJTo6WseOHSt0p+mZnuj5K8nr2bhxY02bNk3vv/++Zs+erczMTP3000/66aef1KdPH7Vv316vv/56kRcYSkuw94f/a3Sy1zX/wlewu3dPdjEt2B2ewbz++utat25dofo2bdqoZ8+ekvKGAZswYYK+/PJLpaWlaf369Vq/fr1Gjhyphg0b6qWXXjLvks2/G/Z0LthFREQErV+/fr369OmjJUuWBNQ3bNhQd9xxh7788stiP8aZxFcavSyK83qfSnHeL6e6Kzo3N1cDBw7UxIkTA+5MjYmJUdu2bXXw4EGlpaUF7JOQkKCvvvpKw4cP18yZM7Vv3z4tW7ZMy5Yt0zvvvKO2bdvqjTfeKFGDuai/OQBUB7TbShfttqLRbqPdVhDttuBotxUP7TbabaeS/x15svd+fHx8wPejfw/x4gyHHey7tbhmz56td955J+CGBLvdrpYtW+r666/X7NmzC+3z4osv6qyzztL48eP166+/atu2bdq2bZvGjh2rxMRE9ejRQ/fcc89px0SCEwD+dO6556pBgwbatGmTtmzZojp16mjhwoU6//zzVb9+/WIdo6iTaf8fU6tceOGF5l0+69atK/OGcr5LL71UjzzyiD744AONGTNG7dq1K3JInJJo37692rdvr2PHjmnp0qVavHix5s2bp927d2vw4MGKiYnRP//5z5Meo2bNmjr33HO1evVq/fzzz0GHuylo4MCBmjlzpu688069+eabJY7b5XJp7dq1kgKHJ/H5fJo+fbqkvJOqYEOTSHnvpaNHj+rbb7/VK6+8ori4uBLHUJryTy4dDocZf2k566yz1KJFC23atEmrVq3SOeeco++++05S4YZdVFSUjh07pkmTJhVryK7ykv/6nGpIovwTzLK8G70kr6eUN9RT79691atXL/3+++/6+eeftWDBAq1cuVLz58/XY489psmTJxe7YVla/F+jrKysIj8D+a95Wb2mGzZsCHpxyH+ol5CQED3wwAN64IEHtGfPHv38889atGiRfvzxR+3atUvdu3fXF198obPPPtv8zJfW78WBAwfUuXNnHT58WK1atdJdd92l1q1bKyUlRYmJiXK5XCVqKJd2fCVVnNf7VJxOZ5Hr8i+onOo79aWXXtKMGTMUHR2tRx99VG3btlVKSooaNWoku92uZ555plBDWcr7Xn/++ef1/PPPKy0tTUuWLNFPP/2kJUuWaNWqVXrooYc0e/bsYvdkAYDqjHZb2aDdFoh2W/HRbitdtNtKF+228kW77czk9yA/2WtQMLmb/9mJjo7WqlWryiQuKW/43O7du8swDHXo0EEdO3ZUamqqmjVrpqioKC1cuDBoglPK++669dZblZGRoSVLlmjRokWaN2+eDh06pJ49e6pmzZpBh0guDiZuAQA/1113nSRp7ty5+vHHH+VyuYo1zFH+xMoulyvo+vxhOayWP0zB6Q7bcrq6dOlizjPz2muvnXIy8JNxuVzasGGDOWl8XFycrr32Wr366qv6/vvvdfvtt0tSkcPdFJQ/J8XkyZN15MiRk2576NAhzZ07V4ZhFDmkx6l8++23On78uEJDQ833myQtXbpUe/bskSSNHz9eCxYsCPovfzirnJycUm+Yno7k5GRJ0s6dO4t8/x88eFArVqzQvn37Snz8/PlF5syZo8WLF+vw4cNq165dwJj9/nGc7E7gpUuXavPmzUXGWRby7/betWuXjh49GnSbrVu3miev+c+jrBT39dyzZ48WL14swzBkt9t13nnn6bHHHtP48eP16aefSpLWrVsXMFxOeYmLizOHeQl2Z6iUd9Eyf11ZvaZjxowx7+z1/5f/Gc3KytJvv/2mXbt2SZLq16+vO+64Q++8845+/PFHORwOeb1e827s/PdKUa+px+PR3//+d3Xv3l0HDx48ZXyTJk3S4cOHlZKSookTJ+rBBx/UhRdeaPZiKOnnsbTjK6lTvd7FsXPnTnk8nqDr8hu3RQ3lJOW9Zvl/rw8//FA9evRQhw4d1KRJE/P3de/evYX2O3DggJYuXWp+zlu1aqV//etfGjlypKZOnarIyEjt379fS5cuLfZzAYDqjnZb2aDddgLttpKh3Va6aLeVHtptZxZfSdFuOzP5f7/8386CDh8+rIyMjIC6xo0bKyQkRE6nM+jzkvLe50uXLlV6evpp93T/5JNPZBiG7rjjDg0fPly333672rRpYyZ7gz12dna21q1bZ37nJyUl6aabblKfPn00f/58XXrppZKKfy4QDAlOAPCTP6TRDz/8oNmzZ8tmsxVrUvaEhARJCjoGfFZWlpYvX17sGEoy+XRJ/Prrr2ZDsLzvlAwLC9Prr78um82mw4cPq3///qd9rDlz5uiWW27RM888U+hH2W636y9/+Yuk4l8M+Pvf/66kpCQdOXJEr732WpEnUT6fT//73/+Uk5OjWrVq6c477yxx7MeOHdO7774rSbrzzjsD5oyYNm2apLy/zcmGu2jVqpX59/vqq69KHMOZyL/j0/91b9GihRo2bKjs7GzzORT0zjvv6B//+IeefvrpEj/mrbfeKrvdrh9++EFz5syRdKKx5++qq66SJH3xxRdBT9ZWrFihzp0766abbtLu3btLHIe/knxGU1JS1KRJE/l8Pn3++edBtxk3bpykvLupy3peveK8ni6XSzfffLMeeughrV69utAxLrzwQnMIpuLOJVParrzySkkqcv6puXPnat++fQoNDdUll1xSnqGZ+vfvr7vvvlsffvhhoXVxcXHm5zj/u+qyyy6TzWYzh2wp6Oeff9aqVau0dOnSYg21lt9Ab968edCeBf7fH/5/x6Le36UdnxWcTqf5vvc3f/587d69WzExMSftJbNr1y7z+yXYPEKbN282hwTy/y158MEH1blzZ82bN6/QPi1btjTnFbPq8wQAlRHttrJBuy0P7TbabcHQbis52m20205HdW63dejQQSEhIVqzZk3QJOeUKVMK1cXGxuqCCy6QVPRnbdSoUercubM6d+58yt/dYL8h0on3auvWrQvtYxiGGZv/6zNmzBjdfvvt6tOnT6F9wsPDzbjP5IYuEpwA4Kddu3aqXbu2Vq9erQULFui8885TgwYNTrlf27ZtJUnbt2/XqFGjzPqDBw/qySefPOUdpv7yhxbYv39/qd2xuH79er3wwguS8oY7yf8BKU/nn3++2bicMmVKiS4e+LvqqqsUExOjzZs3q0+fPgHDbuzevVsjR46UdOJE+lRiY2PVt29fhYSEaObMmfrPf/6j3377LWCbrVu36vHHH9fs2bMVEhKiPn36lGj4FMMwtHz5cv3jH//Q7t271bBhw4BGY3Z2tjnkTP6dzCdz9913S5LWrl1b5J2QZSH/Ofs3NG02mzlvTp8+fcy75KS8E8WPP/5YkydPliQ9/PDDJX7MevXq6eKLL9a2bds0c+ZMhYWF6cYbbyy03f3336/ExEStWLFCL730UsCcAr///rv5el9zzTVq2rRpiePw5z98UXE+2126dJEkDRkyRJMmTTJPEr1erz7++GONHTtWktSjR48ziqs4ivN6hoeHmxcIX3nllYALgC6XSwMHDpTb7VbDhg0DLurs3r1bmzdv1v79+8v8efz73/9WZGSkfvrpJ73xxhsB3wMLFizQyy+/LEn65z//aTZCytstt9wiKa9BOm3atIDGwbJly/Ttt99KOvFd1bRpU91www3y+Xzq1q2btm/fbm6flpam//3vf5LyGl35vU9OJv99vmjRooDvtOzsbH300UcaMWKEWZebm2suF/X+Lu34rNKrV6+AIZNWr16tF198UZL00EMPBcw1VVBycrJ5IeHDDz8MaLgtXbpU//3vf80Gsv9revPNN0uS+vbtq99//92s9/l8Gj16tNLT0xUdHW3JbzMAVFa028oO7TbabbTbaLeVFtpttNtOV3VttyUlJen++++XJHXv3j2gt/u8efM0ePDgoPs98cQTstlsGjFihD777DPzORuGoWnTpmn48OGS8j5rp/q758+BWvAmk/z36pdffhnQ+3f//v165plntGLFCkmBr+mNN96okJAQLVy4UCNGjAhIKG/YsEETJ06UVPxzgWCYgxMA/NhsNl133XUaP368srOzizXMkZR3R1D+ZMp9+/bV6NGjlZCQoE2bNik0NFT//ve/zQbcqbRs2VI2m00HDhxQx44dVa9evSLvwPG3bt063XfffQF1hmEoIyNDO3bskCTVqVNHb7/9dtC5Fw4cOFBo/6J07tw5aGPlVJ555hnNnTtXhw8fVq9evTR16lTzjsLiiomJUf/+/dW1a1d99tlnmjRpkpo0aSKXy6Xt27fL4/HorLPO0n//+99iH7N9+/YaMmSInn/+ef3888+6++67lZSUpPr16+vo0aPm6xcXF6c+ffoUOedLwdfQMAw5nU7t3r3bbLg1bdpUw4cPD7hTbvbs2XI6nQoLC9NNN910ynhvvvlmvfXWW3I6nfryyy/NE9SylpqaqpUrV6pHjx5q3ry5nn76aV1++eW66667tHHjRo0aNUo9evRQ3759VbduXaWnp5sT3Hfp0uW0x9P/29/+piVLlsjpdOq6664z77z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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 439,
       "width": 924
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Bias r\n",
    "columns = ['CDA intervention', 'CDA from-scratch']\n",
    "var_name = 'Group Name'\n",
    "val_name = \"Bias Correlation\"\n",
    "samples = all_samples['bias_r']\n",
    "\n",
    "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n",
    "\n",
    "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "ax = axs[0]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n",
    "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r\")\n",
    "ax.axhline(0)\n",
    "\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"Accuracy delta\"\n",
    "bdf = pd.DataFrame(samples, columns=columns)\n",
    "bdf['deltas'] = bdf['CDA from-scratch'] - bdf['CDA intervention']\n",
    "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "ax = axs[1]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n",
    "            palette='gray')\n",
    "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r deltas\")\n",
    "ax.axhline(0)\n",
    "\n",
    "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do we actually need to do the full multiboostrap, where we sample over both seeds and examples simultaneously? We can check this with ablations where we sample over one axis only:\n",
    "\n",
    "1. Seeds only (`sample_examples=False`)\n",
    "2. Examples only (`sample_seeds=False`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "HV8d-uK-J488"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multibootstrap (unpaired) on 60 examples\n",
      "  Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Base: 125 runs\n",
      "  Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Expt: 125 runs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c68bc0c91fe2428f93e8febe28824520",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.264 with 95% CI of (0.223 to 0.303)\n",
      "  E[L'] = 0.194 with 95% CI of (0.167 to 0.225)\n",
      "  E[L'-L] = -0.0695 with 95% CI of (-0.119 to -0.0197); p-value = 0.005\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 439,
       "width": 924
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title As above, but sample seeds only\n",
    "rseed=42\n",
    "\n",
    "metric = get_bias_corr\n",
    "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                        metric, nboot=num_bootstrap_samples,\n",
    "                                        rng=rseed,\n",
    "                                        paired_seeds=False,\n",
    "                                        sample_examples=False,\n",
    "                                        progress_indicator=tqdm)\n",
    "\n",
    "columns = ['CDA intervention', 'CDA from-scratch']\n",
    "var_name = 'Group Name'\n",
    "val_name = \"Bias Correlation\"\n",
    "\n",
    "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n",
    "\n",
    "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "ax = axs[0]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n",
    "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r\")\n",
    "ax.axhline(0)\n",
    "\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"Accuracy delta\"\n",
    "bdf = pd.DataFrame(samples, columns=columns)\n",
    "bdf['deltas'] = bdf['CDA from-scratch'] - bdf['CDA intervention']\n",
    "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "ax = axs[1]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n",
    "            palette='gray')\n",
    "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r deltas\")\n",
    "ax.axhline(0)\n",
    "\n",
    "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "cellView": "form",
    "id": "91QJmlfvKxhR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multibootstrap (unpaired) on 60 examples\n",
      "  Base seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Base: 125 runs\n",
      "  Expt seeds (25): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "  Expt: 125 runs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0bad3f24e02c4ed9a7dafd369d3b2088",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap statistics from 1000 samples:\n",
      "  E[L]  = 0.259 with 95% CI of (0.118 to 0.39)\n",
      "  E[L'] = 0.193 with 95% CI of (0.0732 to 0.309)\n",
      "  E[L'-L] = -0.0668 with 95% CI of (-0.149 to 0.0121); p-value = 0.053\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 439,
       "width": 918
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title As above, but sample examples only\n",
    "rseed=42\n",
    "\n",
    "metric = get_bias_corr\n",
    "samples = multibootstrap.multibootstrap(selected_runs, preds, labels,\n",
    "                                        metric, nboot=num_bootstrap_samples,\n",
    "                                        rng=rseed,\n",
    "                                        paired_seeds=False,\n",
    "                                        sample_seeds=False,\n",
    "                                        progress_indicator=tqdm)\n",
    "\n",
    "columns = ['CDA intervention', 'CDA from-scratch']\n",
    "var_name = 'Group Name'\n",
    "val_name = \"Bias Correlation\"\n",
    "\n",
    "fig, axs = pyplot.subplots(1, 2, gridspec_kw=dict(width_ratios=[2, 1]), figsize=(15, 7))\n",
    "\n",
    "bdf = pd.DataFrame(samples, columns=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "ax = axs[0]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile')\n",
    "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r\")\n",
    "ax.axhline(0)\n",
    "\n",
    "var_name = 'Pretraining Steps'\n",
    "val_name = \"Accuracy delta\"\n",
    "bdf = pd.DataFrame(samples, columns=columns)\n",
    "bdf['deltas'] = bdf['CDA from-scratch'] - bdf['CDA intervention']\n",
    "bdf = bdf.drop(axis=1, labels=columns).melt(var_name=var_name, value_name=val_name)\n",
    "bdf['x'] = 0\n",
    "ax = axs[1]\n",
    "sns.violinplot(ax=ax, x=var_name, y=val_name, data=bdf, inner='quartile',\n",
    "            palette='gray')\n",
    "ax.set_title(\"MultiBERTs CDA intervention vs. from-scratch - bias r deltas\")\n",
    "ax.axhline(0)\n",
    "\n",
    "multibootstrap.report_ci(samples, c=0.95, expect_negative_effect=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fnJrTw8vKFZ7"
   },
   "source": [
    "In both of the above, we get lower p-values - suggesting that if we don't account jointly for _both_ sources of variation, we could end up making overly-confident conclusions about the difference between these methods."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "last_runtime": {},
   "name": "Application: Gender Bias in Coreference Systems",
   "private_outputs": true,
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
